Computational Intelligence, SS08
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Course Notes (Skriptum)
Online Tutorials
Practical Course Slides
Animated Algorithms
Artificial Neuron
Multi Layer Perceptron
RBF Networks
Optical Character Recognition
Gaussian Mixture Model
Principal Component Analysis
Interactive Tests
Key Definitions
Literature and Links

Single-Layer Perceptron Neural Networks

A single-layer perceptron network consists of one or more artificial neurons in parallel.  The neurons may be of the same type we've seen in the Artificial Neuron Applet.
Layer of several units
  • Each neuron in the layer provides one network output, and is usually connected to all of the external (or environmental) inputs.
  • The applet in this tutorial is an example of a single-neuron, single-layer perceptron network, with just two inputs.
The perceptron learning rule, which we study next, provides a simple algorithm for training a perceptron neural network. However, as we will see, single-layer perceptron networks cannot learn everything: they  are not computationally complete. As mentioned in the introduction, two-input networks cannot approximate the XOR (or XNOR) functions. Of the (22)n or 16 possible functions, a two-input perceptron can only perform 14 functions. As the number of inputs, n, increases, the proportion of functions that can be computed decreases rapidly.

Later, we will investigate multilayer perceptrons.

[Back to the Simple Perceptron Learning applet page ]